CoRe.ADaM: Adaptive Daisy Model to compute core fitness genes

In DepMap-Analytics/CoRe: CoRe R package

Adaptive Daisy Model to compute core fitness genes

Description

This function estimates Core Fitness essential genes using the Adaptive Daisy Model [1] starting from a binary gene dependency matrix.

Usage

CoRe.ADaM(depMat,
          display=TRUE,
          main_suffix='fitness genes in at least 1 cell line',
          xlab='n. dependent cell lines',
          ntrials=1000,
          verbose=TRUE,
          TruePositives)

Arguments

depMat	Binary dependency matrix, rows are genes and columns are samples (screens, cell-cell lines). A 1 in position [i,j] indicates that inactivation of the i-th gene exerts a significant loss of fitness in the j-th sample, 0 otherwise.
display	Boolean, default is TRUE. Should bars indicating dependency profiles and boxes for estimated null models be plotte.
main_suffix	If display=TRUE, title suffix to be give to the plots.
xlab	label to be used in the x-axis of the plots, default is 'n. cell lines'.
ntrials	Integer, default =1000. How many times to randomly perturb dependency matrix to generate null distributions of number of genes called essentials in fixed number of cell lines.
verbose	Boolean, default is TRUE. Should the computation progress be monitored.
TruePositives	Vector of gene symbols to be used as reference prior known essential genes.

Details

This function identifies Core Fitness essential genes from the joint analysis of multiple CRISPR-Cas9 viability screens performed on different cell-lines / models. It works with binary gene x cell-line essantial/non-essential matrices and it estimates the minimal number n of cell-lines in which a gene should be called as essential in order to be considered as a core-fitness essential gene for the tissue of origin of the screened cell-lines. This threshold is computed in a semi-supervised way and it is defined as that maximising the deviance from expectation of the number of genes that are essential in n cell-lines and their true positive rates computed with respect to a set of prior known core-fitness essential genes (to be provided in input).

Value

coreFitnessGenes

A vector of strings with estimated Core Fitness Genes' symbols.

Author(s)

C. Pacini, E. Karakoc, A. Vinceti & F. Iorio

References

[1] Behan FM, Iorio F, Picco G, Gonçalves E, Beaver CM, Migliardi G, et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature. 2019;568:511–6.

[2] Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S, Mero P, Dirks P, Sidhu S, Roth FP, Rissland OS, Durocher D, Angers S, Moffat J. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell. 2015 Dec 3;163(6):1515-26. doi: 10.1016/j.cell.2015.11.015. Epub 2015 Nov 25. PMID: 26627737.

See Also

CoRe.panessprofile CoRe.generateNullModel CoRe.empiricalOdds CoRe.truePositiveRate CoRe.tradeoffEO_TPR CoRe.coreFitnessGenes

Examples

## Downloading dependency matrix
## for > 300 cancer cell lines from [1]
BinDepMat<-CoRe.download_BinaryDepMatrix()

## Extracting dependency submatrix for
## Non-Small Cell Lung Carcinoma cell lines only
LungDepMat<-CoRe.extract_tissueType_SubMatrix(BinDepMat)

## Loading a reference set of essential genes from
## from the CRISPRcleanR package, derived from [1] and [2]
data(curated_BAGEL_essential)

## Computing lung cancer core-fitness genes with ADaM
cfgenes <- CoRe.ADaM(LungDepMat, TruePositives = curated_BAGEL_essential)

DepMap-Analytics/CoRe documentation built on July 6, 2022, 8:01 a.m.